Compressive ultrafast pulse measurement via time-domain single-pixel imaging
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In contrast to imaging using position-resolving cameras, single-pixel imaging uses a bucket detector along with spatially structured illumination to compressively recover images. This emerging imaging technique is a promising candidate for a broad range of applications due to the high signal-to-noise ratio (SNR) and sensitivity, and applicability in a wide range of frequency bands. Here, inspired by single-pixel imaging in the spatial domain, we demonstrate a time-domain single-pixel imaging (TSPI) system that covers frequency bands including both terahertz (THz) and near-infrared (NIR) regions. By implementing a programmable temporal fan-out gate based on a digital micromirror device, we can deterministically prepare temporally structured pulses with a temporal sampling size down to <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mn>16.00</mml:mn> <mml:mo>±</mml:mo> <mml:mn>0.01</mml:mn> <mml:mspace width="thinmathspace"/> <mml:mspace width="thinmathspace"/> <mml:mi mathvariant="normal">f</mml:mi> <mml:mi mathvariant="normal">s</mml:mi> </mml:math> . By inheriting the advantages of detection efficiency and sensitivity from spatial single-pixel imaging, TSPI enables the recovery of a 5 fJ THz pulse and two NIR pulses with over <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mn>97</mml:mn> <mml:mi mathvariant="normal">%</mml:mi> </mml:math> fidelity via compressive sensing. We demonstrate that the TSPI is robust against temporal distortions in the probe pulse train as well. As a direct application, we apply TSPI to machine-learning-aided THz spectroscopy and demonstrate a high sample identification accuracy (97.5%) even under low SNRs (SNR <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mo>∼</mml:mo> </mml:mrow> <mml:mn>10</mml:mn> </mml:math> ).
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it